Merge branch 'main' into release-preview-jamba-models

Author: v-alje

.openpublishing.redirection.azure-monitor.json

@@ -10,6 +10,11 @@
             "redirect_url": "/azure/azure-monitor/vm/scom-managed-instance-overview",
             "redirect_document_id": false
          },
+        {
+            "source_path_from_root": "/articles/azure-monitor/scom-manage-instance/common-questions.md",
+            "redirect_url": "/azure/azure-monitor/scom-manage-instance/scom-managed-instance-faq",
+            "redirect_document_id": false
+        },
         {
             "source_path_from_root": "/articles/azure-monitor/vm/scom-managed-instance-overview.md",
             "redirect_url": "/azure/azure-monitor/scom-manage-instance/overview",

articles/ai-services/immersive-reader/how-to-store-user-preferences.md

@@ -38,6 +38,55 @@ const options = {
 ImmersiveReader.launchAsync(YOUR_TOKEN, YOUR_SUBDOMAIN, YOUR_DATA, options);
 ```
 
+### Example Preferences JSON Structure
+
+Here's an example of what the `value` parameter looks like when parsed, along with the types for each field:
+
+```json
+{
+    "displayOptionsState": {
+        "textSize": "number",
+        "fontFamily": "string",
+        "textSpacing": "number",
+        "formattingEnabled": "boolean",
+        "theme": "string",
+        "themeSetByUser": "boolean",
+        "syllabificationEnabled": "boolean",
+        "nounHighlightingEnabled": "boolean",
+        "nounHighlightingColor": "string",
+        "verbHighlightingEnabled": "boolean",
+        "verbHighlightingColor": "string",
+        "adjectiveHighlightingEnabled": "boolean",
+        "adjectiveHighlightingColor": "string",
+        "adverbHighlightingEnabled": "boolean",
+        "adverbHighlightingColor": "string",
+        "pictureDictionaryEnabled": "boolean",
+        "posLabelsEnabled": "boolean"
+    },
+    "readAloudState": {
+        "readAloudSpeed": "number",
+        "voice": "string"
+    },
+    "translationState": {
+        "shouldTranslateWords": "boolean",
+        "translationLanguage": "string" // encoded JSON
+    }
+}
+```
+
+And here the sample for the `translationLanguage` decoded JSON.
+
+```json
+{
+  "text": "string",
+  "key": "string",
+  "data": {
+    "tlc": "string",
+    "slc": "string"
+  }
+}
+```
+
 ## Load user preferences
 
 Pass in the user's preferences to the Immersive Reader app by using the `-preferences` option. A trivial example to store and load the user's preferences is as follows:

articles/ai-services/immersive-reader/language-support.md

@@ -665,3 +665,28 @@ Immersive Reader supports the following human languages.
 | Spanish (Spain) | es-ES |
 | Swedish | sv |
 | Swedish (Sweden) | sv-SE |
+
+## Math
+
+| Language | Tag |
+|----------|-----|
+| Catalan (Catalan) | ca-ES |
+| Chinese Simplified | zh-Hans |
+| Chinese Traditional | zh-Hant |
+| Danish (Denmark) | da-DK |
+| Dutch (Netherlands) | nl-NL |
+| English (United States) | en-US |
+| Finnish (Finland) | fi-FI |
+| French (France) | fr-FR |
+| German (Germany) | de-DE |
+| Italian (Italy) | it-IT |
+| Japanese (Japan) | ja-JP |
+| Korean (Korea) | ko-KR |
+| Norwegian Bokmal (Norway) | nb-NO |
+| Polish (Poland) | pl-PL |
+| Portuguese (Brazil) | pt-BR |
+| Portuguese (Portugal) | pt-PT |
+| Russian (Russia) | ru-RU |
+| Swedish (Sweden) | sv-SE |
+| Spanish (Spain) | es-ES |
+| Turkish (Türkiye) | tr-TR |

articles/ai-studio/concepts/retrieval-augmented-generation.md

@@ -22,18 +22,18 @@ This article talks about the importance and need for Retrieval Augmented Generat
 
 ## What is RAG?
 
-Some basics first. Large language models (LLMs) like ChatGPT are trained on public internet data which was available at the point in time when they were trained. They can answer questions related to the data they were trained on. This public data might not be sufficient to meet all your needs. You might want questions answered based on your private data. Or, the public data might simply have gotten out of date. The solution to this problem is Retrieval Augmented Generation (RAG), a pattern used in AI which uses an LLM to generate answers with your own data.
+Some basics first. Large language models (LLMs) like ChatGPT are trained on public internet data that was available at the point in time when they were trained. They can answer questions related to the data they were trained on. This public data might not be sufficient to meet all your needs. You might want questions answered based on your private data. Or, the public data might simply have gotten out of date. The solution to this problem is Retrieval Augmented Generation (RAG), a pattern used in AI that uses an LLM to generate answers with your own data.
 
 ## How does RAG work?
 
-RAG is a pattern which uses your data with an LLM to generate answers specific to your data. When a user asks a question, the data store is searched based on user input. The user question is then combined with the matching results and sent to the LLM using a prompt (explicit instructions to an AI or machine learning model) to generate the desired answer. This can be illustrated as follows.
+RAG is a pattern that uses your data with an LLM to generate answers specific to your data. When a user asks a question, the data store is searched based on user input. The user question is then combined with the matching results and sent to the LLM using a prompt (explicit instructions to an AI or machine learning model) to generate the desired answer. This can be illustrated as follows.
 
 :::image type="content" source="../media/index-retrieve/rag-pattern.png" alt-text="Screenshot of the RAG pattern." lightbox="../media/index-retrieve/rag-pattern.png":::
 
 
 ## What is an index and why do I need it?
 
-RAG uses your data to generate answers to the user question. For RAG to work well, we need to find a way to search and send your data in an easy and cost efficient manner to the LLMs. This is achieved by using an index. An index is a data store which allows you to search data efficiently. This is very useful in RAG. An index can be optimized for LLMs by creating vectors (text data converted to number sequences using an embedding model). A good index usually has efficient search capabilities like keyword searches, semantic searches, vector searches or a combination of these. This optimized RAG pattern can be illustrated as follows.
+RAG uses your data to generate answers to the user question. For RAG to work well, we need to find a way to search and send your data in an easy and cost efficient manner to the LLMs. This is achieved by using an index. An index is a data store that allows you to search data efficiently. This is very useful in RAG. An index can be optimized for LLMs by creating vectors (text data converted to number sequences using an embedding model). A good index usually has efficient search capabilities like keyword searches, semantic searches, vector searches or a combination of these. This optimized RAG pattern can be illustrated as follows.
 
 :::image type="content" source="../media/index-retrieve/rag-pattern-with-index.png" alt-text="Screenshot of the RAG pattern with index." lightbox="../media/index-retrieve/rag-pattern-with-index.png":::
 

articles/ai-studio/media/index-retrieve/rag-pattern-with-index.png

@@ -4,7 +4,7 @@ description: Learn how to use Key Management Service (KMS) etcd encryption with
 ms.topic: article
 ms.subservice: aks-security
 ms.custom: devx-track-azurecli
-ms.date: 05/24/2024
+ms.date: 06/19/2024
 ---
 
 # Add Key Management Service etcd encryption to an Azure Kubernetes Service cluster
@@ -36,7 +36,7 @@ The following limitations apply when you integrate KMS etcd encryption with AKS:
 
 * Deleting the key, the key vault, or the associated identity isn't supported.
 * KMS etcd encryption doesn't work with system-assigned managed identity. The key vault access policy must be set before the feature is turned on. System-assigned managed identity isn't available until after the cluster is created. Consider the cycle dependency.
-* Azure Key Vault with a firewall to allow public access isn't supported because it blocks traffic from the KMS plugin to the key vault.
+* Azure Key Vault with a firewall setting "allow public access from specific virtual networks and IP addresses" or "disable public access" isn't supported because it blocks traffic from the KMS plugin to the key vault.
 * The maximum number of secrets that are supported by a cluster that has KMS turned on is 2,000. However, it's important to note that [KMS v2][kms-v2-support] isn't limited by this restriction and can handle a higher number of secrets.
 * Bring your own (BYO) Azure key vault from another tenant isn't supported.
 * With KMS turned on, you can't change the associated key vault mode (public versus private). To [update a key vault mode][update-a-key-vault-mode], you must first turn off KMS, and then turn it on again.
@@ -445,6 +445,7 @@ kubectl get secrets --all-namespaces -o json | kubectl replace -f -
 [az-aks-create]: /cli/azure/aks#az-aks-create
 [az-aks-update]: /cli/azure/aks#az_aks_update
 [turn-on-kms-for-a-public-key-vault]: #turn-on-kms-for-a-public-key-vault
+[azure-keyvault-firewall]:../key-vault/general/how-to-azure-key-vault-network-security.md
 [turn-on-kms-for-a-private-key-vault]: #turn-on-kms-for-a-private-key-vault
 [update-a-key-vault-mode]: #update-a-key-vault-mode
 [api-server-vnet-integration]: api-server-vnet-integration.md

articles/ai-studio/media/index-retrieve/rag-pattern.png

@@ -20,10 +20,10 @@ In this article, we use Azure Pipelines to deploy a Windows container applicatio
 - A working Windows app with Dockerfile hosted on [Azure Repos](https://docs.github.com/get-started/quickstart/create-a-repo). 
 
 ## Add a Service Connection
-Before you create your pipeline, you should first create your Service Connection since you'll be asked to choose and verify your connection when creating your template. A Service Connection allows you to connect to your registry of choice (ACR or Docker Hub) when using the task templates. When adding a new service connection, choose the Docker Registry option. The following form asks you to choose Docker Hub or Azure Container Registry along with pertaining information. To follow along with this tutorial, use Azure Container Registry. You can create a new Service Connection following the directions [here](https://learn.microsoft.com/azure/devops/pipelines/library/service-endpoints?view=azure-devops&tabs=yaml#create-new).
+Before you create your pipeline, you should first create your Service Connection since you'll be asked to choose and verify your connection when creating your template. A Service Connection allows you to connect to your registry of choice (ACR or Docker Hub) when using the task templates. When adding a new service connection, choose the Docker Registry option. The following form asks you to choose Docker Hub or Azure Container Registry along with pertaining information. To follow along with this tutorial, use Azure Container Registry. You can create a new Service Connection following the directions [here](/azure/devops/pipelines/library/service-endpoints).
 
 ## Secure your secrets
-Since we're using sensitive information that you don't want others to access, we use variables to protect our information. Create a variable by following the directions [here](https://learn.microsoft.com/azure/devops/pipelines/process/variables?view=azure-devops&tabs=yaml%2Cbatch#secret-variables).
+Since we're using sensitive information that you don't want others to access, we use variables to protect our information. Create a variable by following the directions [here](/azure/devops/pipelines/process/variables).
 
 To add a Variable, you click the **Variables** button next to the Save button in the top-right of the editing view for your pipeline. Select the **New Variable** button and enter your information. Add the variables below with your own secrets appropriate from each resource.
 

articles/aks/use-kms-etcd-encryption.md

@@ -4,7 +4,7 @@
   items:
   - name: About App Service Environment
     href: overview.md
-  - name: App Service offering comparison
+  - name: Compare with App Service
     href: ase-multi-tenant-comparison.md
   - name: Version comparison
     href: version-comparison.md
@@ -142,4 +142,4 @@
       - name: Open-source technologies FAQ
         href: ../faq-open-source-technologies.yml?toc=%2fazure%2fapp-service%2fenvironment%2ftoc.json
       - name: Configuration and management FAQ
-        href: ../faq-configuration-and-management.yml?toc=%2fazure%2fapp-service%2fenvironment%2ftoc.json
+        href: ../faq-configuration-and-management.yml?toc=%2fazure%2fapp-service%2fenvironment%2ftoc.json